US9087346B2 - Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices - Google Patents

Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices Download PDF

Info

Publication number
US9087346B2
US9087346B2 US14/509,390 US201414509390A US9087346B2 US 9087346 B2 US9087346 B2 US 9087346B2 US 201414509390 A US201414509390 A US 201414509390A US 9087346 B2 US9087346 B2 US 9087346B2
Authority
US
United States
Prior art keywords
data
location
mobile device
household
transitory computer
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
US14/509,390
Other languages
English (en)
Other versions
US20150100426A1 (en
Inventor
Dale Hartzell
Mark Welton
Michael Perri
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Mobile Technology Corp
Original Assignee
Mobile Technology Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority to US14/509,390 priority Critical patent/US9087346B2/en
Application filed by Mobile Technology Corp filed Critical Mobile Technology Corp
Publication of US20150100426A1 publication Critical patent/US20150100426A1/en
Priority to US14/731,281 priority patent/US9439033B2/en
Application granted granted Critical
Publication of US9087346B2 publication Critical patent/US9087346B2/en
Assigned to Mobile Technology Corporation, LLC reassignment Mobile Technology Corporation, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: HARTZELL, DALE, PERRI, MICHAEL, WELTON, MARK
Priority to US15/046,375 priority patent/US9565522B2/en
Priority to US15/046,394 priority patent/US9654919B2/en
Priority to US15/468,905 priority patent/US9870574B2/en
Priority to US15/834,685 priority patent/US10402860B2/en
Priority to US16/449,282 priority patent/US10719852B2/en
Priority to US16/583,185 priority patent/US11392987B2/en
Assigned to Mobile Technology, LLC reassignment Mobile Technology, LLC CHANGE OF NAME (SEE DOCUMENT FOR DETAILS). Assignors: Mobile Technology Corporation, LLC
Priority to US16/866,343 priority patent/US11049145B2/en
Assigned to Mobile Technology, LLC reassignment Mobile Technology, LLC CHANGE OF ADDRESS Assignors: Mobile Technology, LLC
Priority to US17/328,726 priority patent/US11568444B2/en
Assigned to MOBILE TECHNOLOGY CORPORATION reassignment MOBILE TECHNOLOGY CORPORATION CHANGE OF NAME (SEE DOCUMENT FOR DETAILS). Assignors: Mobile Technology, LLC
Priority to US17/807,105 priority patent/US11783372B2/en
Priority to US18/461,339 priority patent/US20230410152A1/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0261—Targeted advertisements based on user location
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0204—Market segmentation
    • G06Q30/0205—Location or geographical consideration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0277—Online advertisement
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/535—Tracking the activity of the user
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/02—Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/50—Secure pairing of devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H04W4/206—
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/20—Services signaling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel
    • H04W4/21—Services signaling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel for social networking applications

Definitions

  • Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices.
  • Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices.
  • the delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices.
  • Some embodiments allow advertisers to better target their ads to relevant target audience with greater accuracy.
  • Some embodiments use location data records from websites, mobile ad networks, mobile applications, and/or networks with sensors located in malls, airports, transportation terminals, hotels, offices, medical offices, elevators, etc.
  • This location data can be used to build location profiles that can be linked to residential addresses through a series of analytical processes.
  • any database that contains a residential address as a data element can be associated with the mobile device to build enhanced services that can be delivered to the mobile device or can be used to provide services that need information on locations and conditions using mobile devices in an area to build that profile.
  • the system may also have the ability to group devices into “social networks” based on analysis of location data overlap for a single location entered into the system, or multiple locations autonomously identified by the system. These social networks can be further analyzed using the corresponding data elements in the linked databases to refine the social networks based on common characteristics found in the data.
  • Embodiments of the present technology also include computer-readable storage media containing sets of instructions to cause one or more processors to perform the methods, variations of the methods, and other operations described herein.
  • FIG. 1 illustrates an example of a networked-based environment in which some embodiments of the present technology may be utilized
  • FIG. 2 illustrates various components and interactions in accordance with one or more embodiments of the present technology
  • FIG. 3 is a block diagram illustrating various data and partner components in accordance with various embodiments of the present technology
  • FIG. 4 is a block diagram illustrating the use of anonymized requests by ad network partners to retrieve data from the system in accordance with some embodiments of the present technology
  • FIG. 5 is a flowchart illustrate an exemplary set of operations for associating mobiles devices with residential addresses in accordance with one or more embodiments of the present technology
  • FIG. 6 illustrates an example of a computer system with which some embodiments of the present technology may be utilized.
  • Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. Some embodiments enable the delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases, which is not currently linked to individual mobile devices accurately.
  • One application of this technology is to allow advertisers to better target their ads to relevant target audience with greater accuracy.
  • the technology uses location data records from mobile ad networks, mobile applications, and hundreds of networks with sensors located in malls, airports, transportation terminals, hotels, offices, medical offices, elevators, etc. This location data can be used to build location profiles that can be linked to residential addresses through a series of analytical processes.
  • any database that contains a residential address as a data element can be associated with the mobile device to build enhanced services that can be delivered to the mobile device or can be used to provide services that need information on locations and conditions using mobile devices in an area to build that profile.
  • This information can also be used to build “social networks” identifying individuals with common interests, associations, and social dynamics to provide additional insight into the mobile user.
  • a substantial amount of data on each individual and household is stored in corporate, retailer, government, and marketing databases.
  • This data can include any type of data collected today—demographic data, psychographic data, behavioral data, purchase data, interest data, crime data, occupation data, registration data, survey data, medical data and more.
  • This data can be used for a variety of purposes that include advertising, marketing, location studies, public safety, healthcare and more.
  • the challenge is linking the mobile device to an individual or household so that the data from these existing databases, which are typically keyed by name and address, can be used to provide enhanced services to the users of mobile devices and expand services for advertisers, businesses, and government leveraging location data from mobile devices. Even where these commercial and government databases have a mobile telephone number in the database, these are still not easily linked to mobile devices for the delivery of other services. Mobile applications and services only have access to device ID keys, mobile data network ID keys, Wi-Fi network keys, Bluetooth IDs, cookies and software defined persistent and transient device identifiers that do not exist in those databases.
  • Identifying the household address associated with a mobile device can be done by mobile carriers from their billing and provisioning databases, but this information is not made available to other service providers and government agencies. To provide enhanced services, these commercial and government agencies need an alternative solution that can accurately identify a mobile devices household address to link to their data that does not rely on mobile carrier data or databases.
  • the challenge is trying to identify the mobile devices in a social group or interest group.
  • Mobile advertising networks, mobile apps, and mobile websites have billions of records associated with mobile transactions that can be mined to create these social networks “graphs” that link devices and hence the individuals together.
  • Various embodiments of the present technology provide a solution to this challenge.
  • embodiments may include a machine-readable medium having stored thereon instructions that may be used to program a computer (or other electronic devices) to perform a process.
  • the machine-readable medium may include, but is not limited to, optical discs, compact disc read-only memories (CD-ROMs), magneto-optical discs, ROMs, random access memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), application-specific integrated circuits (ASICs), magnetic or optical cards, flash memory, or other type of media/machine-readable medium suitable for storing electronic instructions.
  • CD-ROMs compact disc read-only memories
  • ROMs read-only memories
  • RAMs random access memories
  • EPROMs erasable programmable read-only memories
  • EEPROMs electrically erasable programmable read-only memories
  • ASICs application-specific integrated circuits
  • connection or coupling and related terms are used in an operational sense and are not necessarily limited to a direct physical connection or coupling.
  • two devices may be coupled directly, or via one or more intermediary media or devices.
  • devices may be coupled in such a way that information can be passed therebetween, while not sharing any physical connection with one another.
  • connection or coupling exists in accordance with the aforementioned definition.
  • module refers broadly to general or specific-purpose hardware, software, or firmware (or any combination thereof) components. Modules and engines are typically functional components that can generate useful data or other output using specified input(s). A module or engine may or may not be self-contained. Depending upon implementation-specific or other considerations, the modules or engines may be centralized or functionally distributed.
  • An application program also called an “application” may include one or more modules and/or engines, or a module and/or engine can include one or more application programs.
  • FIG. 1 is a block diagram of network-based environment 100 in accordance with one or more embodiments of the present technology.
  • user devices 110 A- 110 N may use network 115 to submit and retrieve information from data delivery platform 120 .
  • User devices 110 A- 110 N can interact with data delivery platform 120 through an application programming interface (API) that runs on the native operating system of the device, such as IOS® or ANDROIDTM.
  • API application programming interface
  • data delivery platform 120 mobile device users can be targeted for the delivery of customized data by data delivery platform 120 using, for example, spatial and temporal analysis to associate data sources with mobile devices.
  • Content management platform 125 enables the delivery of data stored in database 130 to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases, which are not currently linked to individual mobile devices accurately.
  • data delivery platform 120 can use location data records from websites, mobile ad networks, mobile applications, and hundreds of networks with sensors located in malls, airports, transportation terminals, hotels, offices, medical offices, elevators, etc.
  • This location data can be used to build location profiles that can be linked to residential addresses through a series of analytical processes. Using this information, a customized profile can be built around the mobile device.
  • User devices 110 A- 110 N can be any computing device capable of receiving user input as well as transmitting and/or receiving data via the network 115 .
  • user devices 110 A- 110 N can be any device having computer functionality, such as a personal digital assistant (PDA), mobile telephone, smartphone, wearable computing device (e.g., including eyewear, watches, etc.) tablet, or similar device.
  • PDA personal digital assistant
  • User devices 110 A- 110 N can be configured to communicate via network 115 , which may comprise any combination of local area and/or wide area networks, using both wired and wireless communication systems.
  • network 115 uses standard communications technologies and/or protocols.
  • network 115 may include links using technologies such as Ethernet, 802.11, worldwide interoperability for microwave access (WiMAX), 3G, 4G, CDMA, digital subscriber line (DSL), etc.
  • networking protocols used on network 115 may include multiprotocol label switching (MPLS), transmission control protocol/Internet protocol (TCP/IP), User Datagram Protocol (UDP), hypertext transport protocol (HTTP), simple mail transfer protocol (SMTP) and file transfer protocol (FTP).
  • MPLS multiprotocol label switching
  • TCP/IP transmission control protocol/Internet protocol
  • UDP User Datagram Protocol
  • HTTP hypertext transport protocol
  • HTTP simple mail transfer protocol
  • FTP file transfer protocol
  • Data exchanged over network 115 may be represented using technologies and/or formats including hypertext markup language (HTML) or extensible markup language (XML).
  • HTML hypertext markup language
  • XML extensible markup language
  • all or some links can be encrypted using conventional encryption technologies such as secure sockets layer (SSL), transport layer security (TLS), and Internet Protocol security (IPsec).
  • SSL secure sockets layer
  • TLS transport layer security
  • IPsec Internet Protocol security
  • network 115 can be coupled to network 115 using various types of network communication mechanisms. These network communication mechanisms may communicate with other electronic devices by transmitting and receiving wireless signals using licensed, semi-licensed or unlicensed spectrum over network 115 .
  • network 115 may be comprised of multiple networks, even multiple heterogeneous networks, such as one or more border networks, voice networks, broadband networks, service provider networks, Internet Service Provider (ISP) networks, and/or Public Switched Telephone Networks (PSTNs), interconnected via gateways operable to facilitate communications between and among the various networks.
  • ISP Internet Service Provider
  • PSTNs Public Switched Telephone Networks
  • Network 115 can also include third-party communications networks such as a Global System for Mobile (GSM) mobile communications network, a code/time division multiple access (CDMA/TDMA) mobile communications network, a 3rd or 4th generation (3G/4G) mobile communications network (e.g., General Packet Radio Service (GPRS/EGPRS)), Enhanced Data rates for GSM Evolution (EDGE), Universal Mobile Telecommunications System (UMTS), or Long Term Evolution (LTE) network, or other communications networks.
  • GSM Global System for Mobile
  • CDMA/TDMA code/time division multiple access
  • 3G/4G 3rd or 4th generation
  • GPRS/EGPRS General Packet Radio Service
  • EDGE Enhanced Data rates for GSM Evolution
  • UMTS Universal Mobile Telecommunications System
  • LTE Long Term Evolution
  • FIG. 2 illustrates various components and interactions in accordance with one or more embodiments of the present technology.
  • the system may associate the data in business, commercial, and government databases with mobile device data that comes from a variety of suppliers of suppliers that include mobile ad networks, mobile carriers, mobile applications, businesses, Wi-Fi networks, and any other viable source.
  • the components illustrated in FIG. 2 provide some examples of the means for perform various operations described.
  • the system collects mobile device data.
  • This mobile device data may include event data such as, but not limited to the following: mobile network call data, mobile data network registration and usage, mobile device location data, mobile device browsing and web data, transaction data, mobile application data, social media data, purchase data, login data, device sensor data, credit card data, etc.
  • the mobile device event data can include one or more the following fields: 1) a device identifier, such as UDID, MAC address, cookie, or any other permanent or semi-permanent identifier; 2) location information, typically in Latitude and Longitude or addresses; 3) a timestamp that includes date and time, in minutes and seconds. Note that not all data has to contain a timestamp to provide basic matching. A timestamp may be used, in some embodiment, to cross-match data sources with different device identifiers.
  • the mobile device event data can be clustered by location, device identifiers, and time of day. These clusters are then evaluated against household address data. This address data is then used to link the mobile device ID with the other databases.
  • the system anonymizes the data to provide enhance security for the gathered and linked data and ensure that Personally Identifiable Information (PII) is not disclosed to anyone.
  • PII Personally Identifiable Information
  • anonymous IDs can be created so that PII is never disclosed when the data is used by the customer applications.
  • FIG. 3 is a block diagram which illustrates the use of an independent data processor to match the output data from the system to the data provided by ad network partners. Because PII is used in the matching process, the independent data process is used to prevent either the system or the ad network partner from accessing the PII. The output result from the data processor is the linked data that matches from both data sources.
  • the system can collect raw mobile device data and commercial, corporate, and governmental data on individuals from a variety of partners. This data can be processed by the system in FIG. 2 and used to create a System Data Warehouse that contains PII as a key. The system can output the data warehouse into a system file that can be transmitted to other parties, including Independent Data Processors.
  • Partners such as Ad networks also collect customer information from customers of partner services (applications, websites, etc.) as well as registered users of these partner services, which can similarly be accumulated into the Ad Network Data Warehouse.
  • the Ad Network Data Warehouse can also use the PII as a key.
  • the Ad Network Data Warehouse can also be output as a Partner File for transmission to Independent Data Processors.
  • the Independent Data Processor takes the System File and Partner File and compares the PII keys.
  • the Independent data processor creates an output file that contains combined records from the System File and the Partner File only for records with matching PII keys from both files. In some embodiments, if a record with a PII key is unique to only one of the files, it is not included in the output. The merged file is then transmitted to the Ad Network Partner for use. In various embodiments, the system can ensure that no PII data is shared with either the System or the Ad Network Partner for individuals that are not already known to them.
  • FIG. 4 is a block diagram illustrating the use of anonymized requests by ad network partners to retrieve data from the system in accordance with some embodiments of the present technology.
  • the system collects raw mobile device data and commercial, corporate, and governmental data on individuals from a variety of partners. This data is processed by the system in FIG. 2 and used to create a System Data Warehouse that contains PII as a key. The system then processes the System Data Warehouse through an anonymization process that removes or modifies the PII with data that cannot be directly linked to the PII.
  • Anonymized data can be stored, for example, in a real-time accessible System Mobile Data Mart.
  • the Ad Server When a Publisher Website (or mobile app) makes a request to a Partner Ad Server, the Ad Server in turn makes a request to the System Target Data Engine with provides an external interface to the System Data Mart.
  • the System Target Data Engine takes the anonymous key passed in by the Ad Server and looks up the data in the System Mobile Data Mart.
  • the data returned by the System Data Mart is transmitted to the Ad Server, which in turn uses the data to make decisions on what ad to return to the Publisher.
  • FIG. 5 is a flowchart illustrating an exemplary set of operations for associating mobiles devices with residential addresses in accordance with one or more embodiments of the present technology.
  • the operations illustrated in FIG. 5 may be performed by various means including, but not limited to, data analysis platform 120 , content management platform 125 , database 130 , one or more servers, one or more processors, networks and networking hardware, various modules or engines (e.g., a receiving module, a profiling module, a linking module, an association module, etc.), and/or one or more computing systems such as those described in FIG. 6 below.
  • location data can be received from one or more sources during receiving operation 510 .
  • the building operation 520 can build location profiles that can be linked to residential address during linking operation 530 .
  • Association operation 540 can then using this information to associate mobile devices with residential addressed.
  • Various embodiments of the system for linking mobile device data with other databases using spatial and temporal analysis can includes one or more of the following components and processing algorithms, which can be executed on commercially available servers, using real or virtual servers organized into server clusters.
  • the system may perform one or more of the following seven functions:
  • Function 1 Processing of the Mobile Device/Location Event Data
  • Mobile device/location event data can be transmitted to the system in batch file format or in real time via Application Program Interfaces (APIs) provided to the data suppliers.
  • Batch files transferred to the system use standard secure File Transfer Protocol (FTP) technology.
  • Real-time transfers are done per event and use Application Program Interfaces (APIs) built using the WS02 open-source platforms.
  • the APIs may be built using JavaScript Object Notation (JSON) and provide a way for the partner to transmit data to the platform while requesting data.
  • JSON JavaScript Object Notation
  • the elements transmitted for any mobile device/location record via the batch file or AP may contain at a minimum:
  • the mobile event data can be considered PII because it contains a unique identifier for each mobile device. While it can be transmitted to the system from the data provider “in the clear”, typically the mechanism involves a secured connection and the Mobile Device ID data is encoded using an agreed upon obfuscation algorithm, such as hashing, prior to sending the data to the system. Once data is received by the system it ensures that all Mobile Device IDs are obfuscated prior to being stored in the system databases and used for processing. This obfuscation can be performed, for example, by the data provider prior to transmission or by the receiving system using a SHA-1 hashing algorithm, which is a one-way hash that cannot be reversed back to the original data. Any other comparable one-way hashing or encoding algorithm could be substituted for the SHA-1 algorithm.
  • SHA-1 hashing algorithm is a one-way hash that cannot be reversed back to the original data. Any other comparable one-way hashing or encoding algorithm could be substituted for the
  • the incoming mobile event data is processed through a series of filters that organize data in the system by the Mobile Device IDs.
  • the data can be organized so that it can be processed in different priorities or evaluated differently during subsequent processing.
  • filters can include, but are not limited to, the following:
  • Location analysis is the process by which the system analyzes all of the filtered mobile event location data associated with an individual device to identify the locations most frequently associated with the mobile device. This processing uses a Density-Based Scan algorithm to group these data points and find the central location of these groups of data points. Note that any other type of grouping algorithm could be employed.
  • the Density-Based Scan can consider each mobile/location record latitude/longitude pair as a single point for clustering analysis. Clustering is performed for each Device ID using various algorithms. The algorithms may use the following two parameters:
  • the algorithm may identify clusters of points that meet the density requirement of the MinPts within the Eps. Each data point can then be categorized. Some embodiments use the following categories:
  • the result of cluster processing may be a list that contains the core point locations and number of data points associated with the location. These locations can then be ordered from highest to lowest frequency based on the number of data points associated with the location.
  • the location generated is a geo-location coordinate using latitude and longitude, although any location reference system could be used.
  • these locations can be used in one of two ways to associate with data sources: 1) the location identifier, e.g., latitude/longitude, associated with the pair can be compared with location identifiers stored with the data sources. If the data source uses a street address and does not include a location identifier, then as part of the input data processing for these sources the system will generate a location identifier that can be used for comparison. 2) The second method is to convert the locations generated for each device to street addresses (e.g., 123 Main Street, Anytown, Colo., 80301) using commercially available reverse geocoding services or databases. This processing seeks to identify two primary addresses for each device:
  • street addresses e.g., 123 Main Street, Anytown, Colo., 80301
  • the quality of the address returned by the commercial reverse geocoding services varies widely, trying to return the closest street address to the geocode passed in. These addresses are then compared with the addresses used as keys in the commercial databases that contain the profile information. In some embodiments, the system analyzes the returned address against the commercial databases and categorizes it into one of the following categories:
  • These categories can be used to rate the quality of the match returned and improve the quality of the data being provided. These categories can also be used to rate the quality of location data points created from street addresses when creating location data points from street addresses for commercial data sources in the system.
  • the residential address associated with a device can be linked to data provided in any database that uses this as a key element.
  • These data bases can be commercial, business, marketing, government, law enforcement, healthcare or any other database containing household or individual information.
  • Matching devices with this household and individual data using the residential address will result in a one-to-one match for households with only one person, or a many-to-many match for households with multiple individuals—at a multiple individual household there will be many devices associated with the address that need to be matched to the individuals in the household.
  • a multi-family unit such as an apartment building
  • the system uses external data sources that provide data for each individual in a household that may be used to correlate with the characteristics of a location cluster. These data sources can be marketing data providers, online databases such as LinkedIn and Hoover's. Point of Interest databases, or other databases that contain information relevant to a location that can be associated with the location cluster that may be useful in comparing with known data on the individual, such as interests, hobbies, recreational activities, purchases, etc.
  • the identification of individuals within a multi-family residential address is performed in a similar manner with one enhancement. Additional processing is performed first to identify the devices associated with each household in a multi-family residence. This processing uses overlap analysis of the data for each mobile device to determine which devices have a large number of locations in common, indicating the individuals for these devices are frequently together as members of a household are. Once devices are identified with households, the same processing used to identify individuals within households can be performed to identify individuals.
  • Some sources of mobile event data provide data that comes only from commercial or public locations and does not include any residential locations after processing.
  • the data can be linked to other IDs that are already linked to those data sources.
  • This process can use an overlap analysis of the event location and timestamp data from the NR data against the event location and timestamp data from the linked sources.
  • this analysis can build probable matches based on number of overlapping occurrences, and also allows variation in timing of events from the different sources, since finding exact matches is rare.
  • This process is repeated with existing NR data using different parameters or as new NR data is acquired and as new residential data is acquired to improve the results and obtain the highest quality matches possible. Once a match is obtained, all of the household and individual data that was linked to the residential data device ID can now be linked to the NR data device ID also.
  • This process can be performed for any device data that does not include residential locations, such as public Wi-Fi data, Blue-tooth data, Digital-Out-of-Home sensor data, in-store sensor data, etc.
  • Unique groupings of devices can be created through additional location and data analysis. These “social networks” identified can be sold as unique audiences that are used to reach socially connected groups without having to rely on traditional social network sites such as Facebook to provide the data.
  • the additional value of the groups created by the system analysis is that these are real-life groups that socialize in person, not just online groups that may be virtual only.
  • Various embodiments may use various methods for identifying social network groups: 1) for a specific input location or location/date/time, or 2) autonomous multi-location based groups.
  • Each type of group has different benefits to advertisers.
  • Specific location-based groups tend to be larger groups that identify macro audiences, such as audiences that show interest in a particular kind of sporting event, entertainment, or type of retail category.
  • Multi-location groups are smaller groups that exhibit more interest characteristics in common, providing a more focused audience.
  • the processing to identify a social network group from a set of mobile device location or event data can include the following steps:
  • the processing to identify a social network group by the system autonomously is a bit more involved due to the amount of data that must be processed.
  • the steps to create the social network group autonomously in accordance with one or more embodiments, can include:
  • the system can also augment the social networks by overlaying the data from the linked databases to provide characterization of the social groups as well as further segment them by these criteria to create subgroups. This process can be repeated using different time segments and/or new or modified data to improve results, identify changes, and increase the confidence level of the quality of the social groups identified.
  • the mobile devices can be delivered to consumer applications for a variety of commercial, public safety, and other uses.
  • Consumer in this case specifies any mobile, web-based, or other type of application that uses the data to provide a service based on that data.
  • the first implementation supports ad targeting for mobile ad networks by delivering interest data to those networks, but can be used for any application that is based on using mobile devices or location information.
  • the service can provide real-time or historical information from databases to the consumer application.
  • These applications can receive the data from the system as a file transfer or a web-based synchronous or asynchronous service based on JSON or other similar protocols.
  • the two primary modes of providing data to the consumer application are: 1) device specific requests and 2) location requests.
  • a device specific request is designed to return household or individual information associated with a specific device.
  • the consumer application passes the system a mobile device ID, properly encrypted or obfuscated, and the service returns a set of anonymized data associated with that device ID.
  • a location request can take two forms, but in each the consumer application passes in a location, usually in latitude/longitude format, that it is requesting information from the system.
  • the first type of location request generates a combined response for all mobile devices that are within a certain radius of the location.
  • the second generates an individual level response for all the devices within a certain radius of the location.
  • the system uses real-time mobile event data to identify the mobile device IDs near the requested location.
  • the combined response request builds an aggregated view of all the devices. This is often used for marketing type of services, where they are looking for the characteristics of the group.
  • the system combines the data for each data field to be returned and provides a weighed percentage of the values in each data field. For example if one of the data fields was “male”, and there were 10 devices identified near the location with 3 that linked to data that marked the field as “yes”, then the system would return a response to the consumer application that tells it how many total devices, and that “male” is 30%.
  • the individual response request can build an array of all the data by individual device and passes this back to the consumer application. This allows the consumer application to look at each individual's data separately. It is important to note that the system may or may not return the encoded device ID as part of this service.
  • the system does not serve advertisements.
  • the system shares aggregated Buyer Audience Data, i.e. Furniture Buyers, with selected Ad Serving, Publishing or Ad Network partners as described below. There are 4 ways this can be done.
  • Option 1 Provisioning of Audience Level Data at Ad Network Via PII Match
  • the system provides data to an Independent Data Processor to be the third party partner to perform a Personally Identifiable Information (PII) based database match with other NAI Members.
  • PII Personally Identifiable Information
  • PII Personally Identifiable Information
  • the Independent Data Processor appends System Buyer_Audience level information to Partner's file where there is a PII match. After the match occurs all non-matching information can be discarded.
  • Partner ad delivery is done based on anonymous ids and not on personally identifiable information such as UDID or any id associated with personally identifiable information.
  • a forward hashing technique transforming PII-ID's into ANONYMOUS-ID's occurs.
  • ANONYMOUS-IDs and PII-IDs are kept in separate operational environments and do not co-locate by policy.
  • each user active on the Partner network may have at least one Partner anonymous identifiers (ANONYMOUS-IDs) associated with them.
  • Partner ad delivery system can delivers ads based off this identifier ANONYMOUS-ID.
  • ANONYMOUS-ID identifier that a user is on a Partner network.
  • the ad delivery request from the browser request will be associated with the user ANONYMOUS-ID. That browser request can be fulfilled by Partner.
  • Ads using System data are delivered when the ad delivery system sees a user with defined System Attribute 1 set specified for that particular campaign.
  • the System can integrate with partner Ad Servers and Ad Networks such that at the time a request is made within their Ad Serving platform to Get_Offer, i.e., display an advertisement, the Ad Serving platform will make a request of the System Target Data Engine.
  • the Ad Server supplies System with a mobile device identifier, such as a hashed UDID or a location.
  • the system can then return the audience data that is relevant for this device or location.
  • the Ad Server will then use the audience data provided by System to select an ad to display. This is shown in FIG. 4 .
  • the Get_Audience request may have a format that includes:
  • the request tag is a fully qualified URL, with a set of querying parameters:
  • the Audience_Data response tag will have a different format for a device vs. location request. Either type of response can be one of these 3 types: script, image, or i-frame. Each partner will provide System with the required format/syntax for their response tag.
  • the device request response will simply return the targeting information for the hashed device ID sent in the request along with the audience categories that the device belongs to.
  • An example of a response would be:
  • a location request response can return the targeting data for an aggregated audience at a specific location. For example, if System finds 100 people near the location coordinates passed in the request, it will identify that 10 are in D045, 1 is in C001, and 25 are in C004 audiences. The response would look something like:
  • This provides the partner with the total audience size and the audience size by category, so they can decide on quantity vs. quality for their ad decision.
  • the system can integrate with our partner Ad Servers and Ad Networks such that we send them targeting data for every known Audience member in the System database. This can be transmitted periodically using the same type of response format as in Option 2 or as a file transfer using secure FTP.
  • System can also build and provision custom audiences for a partner. For example, if an advertiser wants to show their ad to Hispanic Furniture buyers with income between $100,000 and $150,000. These traits correspond to the System's standard audiences D047 (Hispanic), D104 (Income 100-150), and C001 (Furniture).
  • the ad network would have to notify System of the ad campaign in advance and provide the advertiser's criteria so that the System could provision the audience.
  • Goal for time to provision is ⁇ 1 business day from receipt of the partner request.
  • reporting tools include, but are not limited to:
  • the system enables a number of different solutions to be built that include, but are not limited to:
  • Embodiments of the present technology include various steps and operations, which have been described above. A variety of these steps and operations may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, the steps may be performed by a combination of hardware, software, and/or firmware. As such, FIG. 6 is an example of a computer system 600 with which embodiments of the present technology may be utilized. Computer system 600 is an example of a means for implementing the functionality and performing several of the operations described above.
  • the computer system includes a bus 610 , at least one processor 620 , at least one communication port 630 , a main memory 640 , a removable storage media 650 , a read only memory 660 , and a mass storage 670 .
  • Processor(s) 620 can be any known processor, such as, but not limited to, Intel® lines of processors; AMD® lines of processors; or Motorola® lines of processors.
  • Communication port(s) 630 can be any of an RS-232 port for use with a modem-based dialup connection, a 60/100 Ethernet port, or a Gigabit port using copper or fiber.
  • Communication port(s) 630 may be chosen depending on a network such as a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system 600 connects.
  • LAN Local Area Network
  • WAN Wide Area Network
  • Main memory 640 can be Random Access Memory (RAM) or any other dynamic storage device(s) commonly known in the art.
  • Read only memory 660 can be any static storage device(s) such as Programmable Read Only Memory (PROM) chips for storing static information such as instructions for processor 620 .
  • PROM Programmable Read Only Memory
  • Mass storage 670 can be used to store information and instructions.
  • hard disks such as the Adaptec® family of SCSI drives, an optical disc, an array of disks such as RAID, such as the Adaptec family of RAID drives, or any other mass storage devices may be used.
  • Bus 610 communicatively couples processor(s) 620 with the other memory, storage and communication blocks.
  • Bus 610 can be a PCI/PCI-X or SCSI based system bus depending on the storage devices used.
  • Removable storage media 650 can be any kind of external hard-drives, floppy drives, solid state storage drives, cloud storage systems, IOMEGA® Zip Drives, Compact Disc-Read Only Memory (CD-ROM), Compact Disc-Re-Writable (CD-RW), and/or Digital Video Disk-Read Only Memory (DVD-ROM).
  • CD-ROM Compact Disc-Read Only Memory
  • CD-RW Compact Disc-Re-Writable
  • DVD-ROM Digital Video Disk-Read Only Memory
  • Embodiments of the present technology may be implemented using a combination of one or more modules or engines.
  • a graphical user interface generation module to generation one or more graphical user interface screens to convey results/information and take instructions
  • a general-purpose or special-purpose “communications module” for interfacing with various components and databases
  • a “data gathering module” to collect information from various sources
  • an “anonymization module” to anonymize data
  • a “rating module” to rate the quality of a residential match
  • a “linking module” to link an address to a mobile device
  • social graph module to group devices based on one or more spatial and temporal analyzes
  • a “reporting module” to generate device and location reports, as well as other modules and engines for providing various functionality needed by embodiments of the present technology.
  • various embodiments may incorporate two or more of these modules into a single module and/or associate a portion of the functionality of one or more of these modules with a different module.
  • Each of these modules and engines provides examples of the means for implementing functionality and performing operations described herein.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Finance (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Computer Security & Cryptography (AREA)
  • Data Mining & Analysis (AREA)
  • Human Resources & Organizations (AREA)
  • Primary Health Care (AREA)
  • Tourism & Hospitality (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • Computer Hardware Design (AREA)
  • General Engineering & Computer Science (AREA)
  • Telephonic Communication Services (AREA)
  • Information Transfer Between Computers (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
US14/509,390 2013-10-09 2014-10-08 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices Active US9087346B2 (en)

Priority Applications (12)

Application Number Priority Date Filing Date Title
US14/509,390 US9087346B2 (en) 2013-10-09 2014-10-08 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US14/731,281 US9439033B2 (en) 2013-10-09 2015-06-04 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US15/046,394 US9654919B2 (en) 2013-10-09 2016-02-17 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US15/046,375 US9565522B2 (en) 2013-10-09 2016-02-17 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US15/468,905 US9870574B2 (en) 2013-10-09 2017-03-24 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US15/834,685 US10402860B2 (en) 2013-10-09 2017-12-07 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US16/449,282 US10719852B2 (en) 2013-10-09 2019-06-21 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US16/583,185 US11392987B2 (en) 2013-10-09 2019-09-25 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US16/866,343 US11049145B2 (en) 2013-10-09 2020-05-04 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US17/328,726 US11568444B2 (en) 2013-10-09 2021-05-24 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US17/807,105 US11783372B2 (en) 2013-10-09 2022-06-15 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US18/461,339 US20230410152A1 (en) 2013-10-09 2023-09-05 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201361888950P 2013-10-09 2013-10-09
US14/509,390 US9087346B2 (en) 2013-10-09 2014-10-08 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US14/731,281 Continuation US9439033B2 (en) 2013-10-09 2015-06-04 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices

Publications (2)

Publication Number Publication Date
US20150100426A1 US20150100426A1 (en) 2015-04-09
US9087346B2 true US9087346B2 (en) 2015-07-21

Family

ID=52777729

Family Applications (9)

Application Number Title Priority Date Filing Date
US14/509,390 Active US9087346B2 (en) 2013-10-09 2014-10-08 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US14/731,281 Active US9439033B2 (en) 2013-10-09 2015-06-04 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US15/046,375 Active US9565522B2 (en) 2013-10-09 2016-02-17 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US15/046,394 Active US9654919B2 (en) 2013-10-09 2016-02-17 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US15/468,905 Active US9870574B2 (en) 2013-10-09 2017-03-24 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US15/834,685 Active US10402860B2 (en) 2013-10-09 2017-12-07 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US16/449,282 Active US10719852B2 (en) 2013-10-09 2019-06-21 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US16/866,343 Active US11049145B2 (en) 2013-10-09 2020-05-04 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US17/328,726 Active US11568444B2 (en) 2013-10-09 2021-05-24 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices

Family Applications After (8)

Application Number Title Priority Date Filing Date
US14/731,281 Active US9439033B2 (en) 2013-10-09 2015-06-04 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US15/046,375 Active US9565522B2 (en) 2013-10-09 2016-02-17 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US15/046,394 Active US9654919B2 (en) 2013-10-09 2016-02-17 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US15/468,905 Active US9870574B2 (en) 2013-10-09 2017-03-24 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US15/834,685 Active US10402860B2 (en) 2013-10-09 2017-12-07 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US16/449,282 Active US10719852B2 (en) 2013-10-09 2019-06-21 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US16/866,343 Active US11049145B2 (en) 2013-10-09 2020-05-04 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US17/328,726 Active US11568444B2 (en) 2013-10-09 2021-05-24 Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices

Country Status (5)

Country Link
US (9) US9087346B2 (fr)
EP (1) EP3039569A4 (fr)
CN (1) CN106062731B (fr)
CA (1) CA2926413A1 (fr)
WO (1) WO2015054291A1 (fr)

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9877153B1 (en) * 2016-10-18 2018-01-23 International Business Machines Corporation Mobile device path and user association
US10360244B2 (en) 2015-09-24 2019-07-23 Liveramp, Inc. System and method for improving computational efficiency of consumer databases using household links
US10402860B2 (en) 2013-10-09 2019-09-03 Mobile Technology Corporation, LLC Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US10687174B1 (en) 2019-09-25 2020-06-16 Mobile Technology, LLC Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US11361630B1 (en) 2020-11-20 2022-06-14 Bank Of America Corporation Identifying and logging mobile devices posing security threats
US11392987B2 (en) 2013-10-09 2022-07-19 Mobile Technology Corporation Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US11882452B2 (en) 2020-11-20 2024-01-23 Bank Of America Corporation Monitoring for security threats associated with mobile devices that have been identified and logged

Families Citing this family (43)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8978158B2 (en) * 2012-04-27 2015-03-10 Google Inc. Privacy management across multiple devices
US9881301B2 (en) 2012-04-27 2018-01-30 Google Llc Conversion tracking of a user across multiple devices
US9514446B1 (en) * 2012-04-27 2016-12-06 Google Inc. Remarketing content to a user associated with multiple devices
US10417653B2 (en) 2013-01-04 2019-09-17 PlaceIQ, Inc. Inferring consumer affinities based on shopping behaviors with unsupervised machine learning models
US10417251B2 (en) * 2014-10-31 2019-09-17 The Boeing Company System and method for storage and analysis of time-based data
CN106341735A (zh) * 2015-07-07 2017-01-18 阿里巴巴集团控股有限公司 一种信息推送方法和装置
US10497023B2 (en) * 2015-10-02 2019-12-03 Adobe Inc. Generating persistent profile identifiers
US20180352428A1 (en) * 2015-12-07 2018-12-06 Report It, LLC Reporting service hybrid web/mobile application platform system and methods
US10467635B1 (en) * 2016-01-28 2019-11-05 Numerify, Inc. Efficient cross customer analytics
US10200483B2 (en) * 2016-01-29 2019-02-05 International Business Machines Corporation Organizing location-based social network communities
US20170287010A1 (en) * 2016-04-04 2017-10-05 Bulzi Media Inc. Enhanced mobile device beacon system
EP3449596B1 (fr) 2016-04-28 2019-07-31 Telefonaktiebolaget LM Ericsson (publ) Technique de gestion de données de performances associées au niveau de service pour des terminaux d'utilisateur itinérants
US11030651B2 (en) * 2016-05-06 2021-06-08 Adp, Llc Segmented user profiles
KR20230051620A (ko) * 2016-06-12 2023-04-18 애플 인크. 제어가능한 외부 디바이스들을 관리하기 위한 사용자 인터페이스
WO2018044768A1 (fr) * 2016-08-29 2018-03-08 Wal-Mart Stores, Inc. Identification basée sur une analyse mobile
US10192071B2 (en) * 2016-09-02 2019-01-29 Symantec Corporation Method for integrating applications
SE541587C2 (en) * 2017-02-22 2019-11-12 Q Matic Ab Computer-implemented system, method & computer program product
US20180259622A1 (en) * 2017-03-08 2018-09-13 Raytheon Company Frequency domain sampling technique for target identification
US10778648B2 (en) * 2017-05-10 2020-09-15 Nexmo Inc. Systems and methods for regional data storage and data anonymization
US11202230B2 (en) 2017-05-19 2021-12-14 Telefonaktiebolaget Lm Ericsson (Publ) Technique for enabling multipath transmission
US11430007B2 (en) * 2017-06-14 2022-08-30 Comcast Cable Communications, Llc Methods and systems for mapping advertising inventory
US10419883B2 (en) * 2017-07-31 2019-09-17 4Info, Inc. Systems and methods for statistically associating mobile devices and non-mobile devices with geographic areas
US20190068726A1 (en) * 2017-08-24 2019-02-28 iAccept Softwares Private Limited System and method for objective verification of physical address of a user / respondent
CN112492589B (zh) 2017-09-29 2022-11-25 华为技术有限公司 一种访问设备标识符的方法及装置
CN108510318A (zh) * 2018-03-21 2018-09-07 口碑(上海)信息技术有限公司 基于地址围栏探测用户到达商圈的方法及装置
US10932118B1 (en) * 2018-05-25 2021-02-23 El Toro.Com, Llc Systems, methods, and apparatuses for providing content according to geolocation
CN109064179B (zh) * 2018-07-11 2022-05-20 成都理工大学 移动支付安全态势感知系统
US11361105B2 (en) * 2018-08-27 2022-06-14 Koninklijke Philips N.V. Time blocking noising for de-identification
US10956454B2 (en) * 2018-09-25 2021-03-23 Microsoft Technology Licensing, Llc Probabilistically generated identity database system and method
US10904029B2 (en) 2019-05-31 2021-01-26 Apple Inc. User interfaces for managing controllable external devices
US11363071B2 (en) 2019-05-31 2022-06-14 Apple Inc. User interfaces for managing a local network
CN110222253A (zh) * 2019-06-12 2019-09-10 北京睦合达信息技术股份有限公司 一种数据采集方法、设备及计算机可读存储介质
WO2021061897A1 (fr) * 2019-09-25 2021-04-01 Mobile Technology, LLC Systèmes et procédés d'utilisation d'une analyse spatiale et temporelle pour associer des sources de données à des dispositifs mobiles
WO2021163707A1 (fr) * 2020-02-14 2021-08-19 Balcony Labs Inc. Système et procédé d'anonymat et de confidentialité sélectifs basés sur un emplacement
US11079913B1 (en) 2020-05-11 2021-08-03 Apple Inc. User interface for status indicators
US20210409204A1 (en) * 2020-06-30 2021-12-30 Bank Of America Corporation Encryption of protected data for transmission over a web interface
US11544695B2 (en) * 2020-09-10 2023-01-03 Block, Inc. Transaction identification by comparison of merchant transaction data and context data
US11055692B1 (en) 2020-09-10 2021-07-06 Square, Inc. Application integration for contactless payments
US11475426B2 (en) * 2020-12-15 2022-10-18 Toast, Inc. System and method for transaction handoff and completion employing ephemeral token
US11475427B2 (en) 2020-12-15 2022-10-18 Toast, Inc. Server for transaction handoff and completion employing ephemeral token
US11651344B2 (en) * 2020-12-15 2023-05-16 Toast, Inc. System and method for transaction handoff and completion employing indirect token
US11651342B2 (en) 2020-12-15 2023-05-16 Toast, Inc. Point-of-sale terminal for transaction handoff and completion employing ephemeral token
CN116668192B (zh) * 2023-07-26 2023-11-10 国网山东省电力公司信息通信公司 一种网络用户行为异常检测方法及系统

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020111172A1 (en) 2001-02-14 2002-08-15 Dewolf Frederik M. Location based profiling
US20080243815A1 (en) * 2007-03-30 2008-10-02 Chan James D Cluster-based assessment of user interests
US20100004997A1 (en) 2008-05-27 2010-01-07 Chand Mehta Methods and apparatus for generating user profile based on periodic location fixes
US20110208425A1 (en) 2010-02-23 2011-08-25 Microsoft Corporation Mining Correlation Between Locations Using Location History
US20120058782A1 (en) 2010-09-03 2012-03-08 Research In Motion Limited Method and apparatus for generating and using location information
US8135505B2 (en) 2007-04-27 2012-03-13 Groupon, Inc. Determining locations of interest based on user visits
US20120088525A1 (en) 2010-10-12 2012-04-12 Kddi Corporation Estimation of significant places visited by mobile-terminal user based on communications log to base stations
US20120108259A1 (en) 2010-11-01 2012-05-03 Wavemarket Inc. System and method for aggregating and associating mobile device location data
US8521593B2 (en) 2011-09-07 2013-08-27 Millennial Media, Inc. Methods and systems for providing mobile advertising using data networks based on groupings associated with internet-connectable devices
US20130345957A1 (en) * 2012-06-22 2013-12-26 Google Inc. Ranking nearby destinations based on visit likelihoods and predicting future visits to places from location history
US8792909B1 (en) 2013-12-04 2014-07-29 4 Info, Inc. Systems and methods for statistically associating mobile devices to households

Family Cites Families (36)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9420423B1 (en) * 2005-04-12 2016-08-16 Ehud Mendelson RF beacon deployment and method of use
CN1822005A (zh) * 2006-04-07 2006-08-23 张天山 基于网站自动生成和搜索引擎的信息推送系统及方法
US7818489B2 (en) * 2006-11-04 2010-10-19 Virident Systems Inc. Integrating data from symmetric and asymmetric memory
JP5340567B2 (ja) 2007-07-24 2013-11-13 株式会社エヌ・ティ・ティ・ドコモ 通信端末、情報管理装置及びプログラム
KR101195630B1 (ko) * 2007-11-14 2012-10-31 콸콤 인코포레이티드 프로파일에 기반하여 타깃 콘텐츠 메시지들의 적절성을 결정하기 위해 지리적 사용자 프로파일을 결정하기 위한 방법들 및 시스템들
US8732246B2 (en) * 2008-03-14 2014-05-20 Madhavi Jayanthi Mobile social network for facilitating GPS based services
US8585632B2 (en) * 2009-07-31 2013-11-19 Covidien LLP Single port device having integral filter/vent
US8275649B2 (en) * 2009-09-18 2012-09-25 Microsoft Corporation Mining life pattern based on location history
US20110071811A1 (en) * 2009-09-18 2011-03-24 International Business Machines Corporation Using event correlation and simulation in authorization decisions
US8756010B2 (en) * 2009-10-12 2014-06-17 Qualcomm Incorporated Method and apparatus for identification of points of interest within a predefined area
US20110213628A1 (en) * 2009-12-31 2011-09-01 Peak David F Systems and methods for providing a safety score associated with a user location
US20110288917A1 (en) * 2010-05-21 2011-11-24 James Wanek Systems and methods for providing mobile targeted advertisements
CN103329560B (zh) 2010-08-26 2017-02-08 泰瑞克·塔勒布 用于创建为社交网络定制的多媒体内容频道的系统和方法
US8526968B2 (en) * 2011-02-14 2013-09-03 Andrew Llc System and method for mobile location by dynamic clustering
US20120259791A1 (en) 2011-04-07 2012-10-11 Sabina Zoidze System and method for social networking
US10034135B1 (en) 2011-06-08 2018-07-24 Dstillery Inc. Privacy-sensitive methods, systems, and media for geo-social targeting
CN102291435B (zh) * 2011-07-15 2012-09-05 武汉大学 一种基于地理时空数据的移动信息搜索及知识发现系统
US10127563B2 (en) * 2011-09-15 2018-11-13 Stephan HEATH System and method for providing sports and sporting events related social/geo/promo link promotional data sets for end user display of interactive ad links, promotions and sale of products, goods, gambling and/or services integrated with 3D spatial geomapping, company and local information for selected worldwide locations and social networking
US10127564B2 (en) * 2011-09-15 2018-11-13 Stephan HEATH System and method for using impressions tracking and analysis, location information, 2D and 3D mapping, mobile mapping, social media, and user behavior and information for generating mobile and internet posted promotions or offers for, and/or sales of, products and/or services
US10349236B2 (en) 2011-10-31 2019-07-09 Intersection Design And Technology, Inc. Web-level engagement and analytics for the physical space
US9087273B2 (en) 2011-11-15 2015-07-21 Facebook, Inc. Facial recognition using social networking information
US9082082B2 (en) 2011-12-06 2015-07-14 The Trustees Of Columbia University In The City Of New York Network information methods devices and systems
US9298826B2 (en) 2012-01-05 2016-03-29 International Business Machines Corporation Goal-oriented user matching among social networking environments
US8989775B2 (en) * 2012-02-29 2015-03-24 RetailNext, Inc. Method and system for WiFi-based identification of person tracks
US9407860B2 (en) 2012-04-06 2016-08-02 Melvin Lee Barnes, JR. System, method and computer program product for processing image data
WO2013157015A2 (fr) * 2012-04-16 2013-10-24 Chunilal Rathod Yogesh Procédé et système pour afficher des actions dynamiques et accessibles avec des identifiants uniques et des activités
WO2013192586A2 (fr) * 2012-06-22 2013-12-27 Google Inc. Déduction d'intérêts d'un utilisateur
US8781735B2 (en) * 2012-06-25 2014-07-15 Google Inc. Adaptive clustering of locations
US9445391B2 (en) 2012-09-21 2016-09-13 Telefonaktiebolaget L M. Ericsson Automatic detection of device type for filtering of data
US20140222570A1 (en) * 2012-11-17 2014-08-07 Teoco Corporation System, Method, and Computer Program Product For Probabilistically Derived Predictive Location Based Targeted Promotion
US9838834B2 (en) * 2012-12-21 2017-12-05 Facebook, Inc. Techniques for wireless transmitter location detection
US9154915B2 (en) 2013-04-16 2015-10-06 Google Inc. Apparatus and method for ascertaining the operating hours of a business
CA2923041A1 (fr) * 2013-09-26 2015-04-02 Mark W. Publicover Fourniture de contenu cible fondee sur les valeurs de l'utilisateur
EP3039569A4 (fr) 2013-10-09 2017-01-25 Mobile Technology Corporation, LLC Systèmes et procédés permettant d'utiliser une analyse spatiale et temporelle pour associer des sources de données à des dispositifs mobiles
US9291700B1 (en) 2015-01-09 2016-03-22 NinthDecimal, Inc. Systems and methods to identify home addresses of mobile devices
US10419883B2 (en) 2017-07-31 2019-09-17 4Info, Inc. Systems and methods for statistically associating mobile devices and non-mobile devices with geographic areas

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020111172A1 (en) 2001-02-14 2002-08-15 Dewolf Frederik M. Location based profiling
US20080243815A1 (en) * 2007-03-30 2008-10-02 Chan James D Cluster-based assessment of user interests
US8135505B2 (en) 2007-04-27 2012-03-13 Groupon, Inc. Determining locations of interest based on user visits
US20100004997A1 (en) 2008-05-27 2010-01-07 Chand Mehta Methods and apparatus for generating user profile based on periodic location fixes
US20110208425A1 (en) 2010-02-23 2011-08-25 Microsoft Corporation Mining Correlation Between Locations Using Location History
US20120058782A1 (en) 2010-09-03 2012-03-08 Research In Motion Limited Method and apparatus for generating and using location information
US20120088525A1 (en) 2010-10-12 2012-04-12 Kddi Corporation Estimation of significant places visited by mobile-terminal user based on communications log to base stations
US20120108259A1 (en) 2010-11-01 2012-05-03 Wavemarket Inc. System and method for aggregating and associating mobile device location data
US8521593B2 (en) 2011-09-07 2013-08-27 Millennial Media, Inc. Methods and systems for providing mobile advertising using data networks based on groupings associated with internet-connectable devices
US20130345957A1 (en) * 2012-06-22 2013-12-26 Google Inc. Ranking nearby destinations based on visit likelihoods and predicting future visits to places from location history
US8792909B1 (en) 2013-12-04 2014-07-29 4 Info, Inc. Systems and methods for statistically associating mobile devices to households

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
Decision Granting Ex Parte Reexamination of U.S. Patent No. 8,792,909, mailed Dec. 19, 2014 (20 pages).
International Search Report and Written Opinion, International Patent Application No. PCT/US14/59541, mailed Jan. 15, 2015. 17 pages.
Isaacman, Sibren et al. "Identifying Important Places in People's Lives from Cellular Network Data," Lecture Notes in Computer Science, vol. 6696, 2011, pp. 133-151 (2011).
Noulas, Anastasios et al. "Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-Based Social Networks," in The Social Mobile Web. (2011), 4 pages.
Office Action received for U.S. Appl. No. 14/096,597. Mailed Feb. 27, 2014 by the U.S. Patent and Trademark Office. 8 pages.
Request for Ex Parte Reexamination of U.S. Patent No. 8,792,909, as filed Nov. 24, 2014 (exhibits omitted) (164 pages).
Zhou, Changqing et al. "Discovering Personally Meaningful Places: An Interactive Clustering Approach." ACM Trans. Inform. Syst. 25, 3, Article 12 (Jul. 2007), 31 pages.
Zoubin Gharamani, "Unsupervised Learning," Gatsby Computational Neuroscience Unit, University College London, UK, Sep. 16, 2004 (32 pages).

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11392987B2 (en) 2013-10-09 2022-07-19 Mobile Technology Corporation Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US10402860B2 (en) 2013-10-09 2019-09-03 Mobile Technology Corporation, LLC Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US11783372B2 (en) 2013-10-09 2023-10-10 Mobile Technology Corporation Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US11568444B2 (en) 2013-10-09 2023-01-31 Mobile Technology Corporation Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US10719852B2 (en) 2013-10-09 2020-07-21 Mobile Technology, LLC Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US11049145B2 (en) 2013-10-09 2021-06-29 Mobile Technology, LLC Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US10360244B2 (en) 2015-09-24 2019-07-23 Liveramp, Inc. System and method for improving computational efficiency of consumer databases using household links
US10448197B2 (en) 2016-10-18 2019-10-15 International Business Machines Corporation Mobile device path and user association
US11070937B2 (en) 2016-10-18 2021-07-20 International Business Machines Corporation Mobile device path and user association
US9877153B1 (en) * 2016-10-18 2018-01-23 International Business Machines Corporation Mobile device path and user association
US11356808B2 (en) 2019-09-25 2022-06-07 Mobile Technology Corporation Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US10687174B1 (en) 2019-09-25 2020-06-16 Mobile Technology, LLC Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US11361630B1 (en) 2020-11-20 2022-06-14 Bank Of America Corporation Identifying and logging mobile devices posing security threats
US11882452B2 (en) 2020-11-20 2024-01-23 Bank Of America Corporation Monitoring for security threats associated with mobile devices that have been identified and logged

Also Published As

Publication number Publication date
US9565522B2 (en) 2017-02-07
CA2926413A1 (fr) 2015-04-16
US20160165390A1 (en) 2016-06-09
US10402860B2 (en) 2019-09-03
US20200265475A1 (en) 2020-08-20
US20210279767A1 (en) 2021-09-09
US20180096393A1 (en) 2018-04-05
CN106062731A (zh) 2016-10-26
US9654919B2 (en) 2017-05-16
CN106062731B (zh) 2019-07-02
US20150100426A1 (en) 2015-04-09
US20190303971A1 (en) 2019-10-03
US11568444B2 (en) 2023-01-31
EP3039569A4 (fr) 2017-01-25
EP3039569A1 (fr) 2016-07-06
US20160165389A1 (en) 2016-06-09
US10719852B2 (en) 2020-07-21
WO2015054291A1 (fr) 2015-04-16
US9870574B2 (en) 2018-01-16
US9439033B2 (en) 2016-09-06
US20150271635A1 (en) 2015-09-24
US20170200196A1 (en) 2017-07-13
US11049145B2 (en) 2021-06-29

Similar Documents

Publication Publication Date Title
US11568444B2 (en) Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US11783372B2 (en) Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US20160224901A1 (en) Multiple device correlation
US20100312706A1 (en) Network centric system and method to enable tracking of consumer behavior and activity
US9854450B2 (en) Enhanced data collection, processing, and analysis facilities
US20160140580A1 (en) Customer demographic information system and method
US20140114774A1 (en) Methods and systems for determining use and content of pymk based on value model
US20220264254A1 (en) Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices
US20150033330A1 (en) Collection and analysis of customer data from application programming interface usage
US20160110728A1 (en) Enhanced data collection, processing, and analysis
US20180247379A1 (en) Evaluating potential connections based on instrumental variables
WO2021061897A1 (fr) Systèmes et procédés d'utilisation d'une analyse spatiale et temporelle pour associer des sources de données à des dispositifs mobiles
US11228866B1 (en) Identification of location-tracked audiences

Legal Events

Date Code Title Description
STCF Information on status: patent grant

Free format text: PATENTED CASE

AS Assignment

Owner name: MOBILE TECHNOLOGY CORPORATION, LLC, COLORADO

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:HARTZELL, DALE;WELTON, MARK;PERRI, MICHAEL;REEL/FRAME:037416/0091

Effective date: 20151217

MAFP Maintenance fee payment

Free format text: PAYMENT OF MAINTENANCE FEE, 4TH YR, SMALL ENTITY (ORIGINAL EVENT CODE: M2551); ENTITY STATUS OF PATENT OWNER: SMALL ENTITY

Year of fee payment: 4

AS Assignment

Owner name: MOBILE TECHNOLOGY, LLC, COLORADO

Free format text: CHANGE OF NAME;ASSIGNOR:MOBILE TECHNOLOGY CORPORATION, LLC;REEL/FRAME:052189/0925

Effective date: 20180827

AS Assignment

Owner name: MOBILE TECHNOLOGY, LLC, COLORADO

Free format text: CHANGE OF ADDRESS;ASSIGNOR:MOBILE TECHNOLOGY, LLC;REEL/FRAME:052579/0080

Effective date: 20200505

AS Assignment

Owner name: MOBILE TECHNOLOGY CORPORATION, COLORADO

Free format text: CHANGE OF NAME;ASSIGNOR:MOBILE TECHNOLOGY, LLC;REEL/FRAME:058014/0675

Effective date: 20200609

MAFP Maintenance fee payment

Free format text: PAYMENT OF MAINTENANCE FEE, 8TH YR, SMALL ENTITY (ORIGINAL EVENT CODE: M2552); ENTITY STATUS OF PATENT OWNER: SMALL ENTITY

Year of fee payment: 8